# agent-trace: The Black Box for AI Agent Debugging — Record and Replay Every Tool Call in Full

> agent-trace is an observation tool designed specifically for AI agents. It can capture every tool call, prompt, and response from MCP clients like Claude Code and Cursor, helping developers debug complex workflows.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-09T08:11:49.000Z
- 最近活动: 2026-04-09T08:15:20.399Z
- 热度: 165.9
- 关键词: AI 智能体, 调试工具, 可观测性, MCP, Claude Code, Cursor, 追踪, 日志, Datadog, Honeycomb, OpenTelemetry
- 页面链接: https://www.zingnex.cn/en/forum/thread/agent-trace-ai
- Canonical: https://www.zingnex.cn/forum/thread/agent-trace-ai
- Markdown 来源: floors_fallback

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## Introduction / Main Floor: agent-trace: The Black Box for AI Agent Debugging — Record and Replay Every Tool Call in Full

agent-trace is an observation tool designed specifically for AI agents. It can capture every tool call, prompt, and response from MCP clients like Claude Code and Cursor, helping developers debug complex workflows.

## Core Features: Full-Link Capture

The design goal of agent-trace is to provide **full observability** of AI agent behaviors. It can:

## 1. Capture Every Tool Call

Modern AI agents complete complex tasks by calling external tools (such as file reading/writing, code execution, API requests). agent-trace records the parameters, execution time, and return results of each tool call, forming a complete call chain.

## 2. Save Complete Prompts and Responses

It not only records the requests sent by the agent but also includes the user's original prompt and the model's complete response. This is crucial for understanding the agent's decision-making logic.

## 3. Session Replay Capability

The captured trace data can be fully replayed. Developers can reproduce the agent's entire execution process like watching a video, precisely locating the moment when a problem occurs.

## 4. Multi-Client Compatibility

agent-trace works based on the MCP (Multi-Client Protocol) and supports:
- Claude Code
- Cursor
- Other MCP-compatible AI clients

## Debug Complex Workflows

When an agent performs multi-step tasks (e.g., "analyze codebase, identify performance bottlenecks, generate optimization plans"), errors in any intermediate step may cause the final result to deviate from expectations. agent-trace allows developers to check each decision point step by step.

## Audit and Compliance

In an enterprise environment, understanding which data the AI system accessed and which operations it performed is a basic requirement for compliance audits. agent-trace provides non-repudiable operation logs.